Risk Factors and Management of Incisional Cerebrospinal Fluid Leakage After Craniotomy: A Retrospective International Multicenter Study.

BACKGROUND: Incisional cerebrospinal fluid (iCSF) leakage is a serious complication after intradural cranial surgery. OBJECTIVE: To determine the incidence and risk factors of iCSF leakage after craniotomy. Secondarily, the complications after iCSF leakage and the success rate of iCSF leakage treatment was studied. METHODS: All patients who underwent an intradural cranial surgery from 2017 to 2018 at 5 neurosurgical centers were retrospectively included. Data were retrieved from medical records with 2 months of follow-up. First, univariate regression analyses were performed. Subsequently, identified risk factors were evaluated in a multivariate regression analysis. RESULTS: In total 2310 consecutive patients were included. Total iCSF leakage rate was 7.1% (n = 165). Younger age, male, higher body mass index, smoking, infratentorial surgery, and use of a dural substitute were associated with increased iCSF leakage risk, and use of a sealant reduced that risk. The odds for developing a wound infection and/or meningitis were 15 times higher in patients with iCSF leakage compared with patients without leakage. Initial conservative iCSF leakage treatment failed in 48% of patients. In 80% of cases, external cerebrospinal fluid drainage ceased the iCSF leakage. A total of 32% of patients with iCSF leakage required wound revision surgery. CONCLUSION: iCSF leakage risk increases by younger age, higher body mass index, smoking, infratentorial craniotomy, and dural substitute use, whereas sealant use reduced the risk for iCSF leakage. The leak increases the risk of postoperative infections. When iCSF leakage occurs, immediate external cerebrospinal fluid drainage or wound revision should be considered.

Cerebrospinal fluid leakage costs after craniotomy and health economic assessment of incidence reduction from a hospital perspective in the Netherlands.

OBJECTIVES: We aim to quantify the cost difference between patients with incisional cerebrospinal fluid (iCSF) leakage and those without after intradural cranial surgery. Second, the potential cost savings per patient when a decrease in iCSF leakage rate would be achieved with and without added costs for preventative measures of various price and efficacy are modelled. DESIGN: Health economic assessment from a hospital perspective based on a retrospective cohort study. SETTING: Dutch tertiary referral centre. PARTICIPANTS: We included 616 consecutive patients who underwent intradural cranial surgery between 1 September 2017 and 1 September 2018. Patients undergoing burr hole surgery or transsphenoidal surgery, or who died within 1 month after surgery or were lost to follow-up were excluded. PRIMARY AND SECONDARY OUTCOME MEASURES: Outcomes of the cost analysis include a detailed breakdown of mean costs per patient for patients with postoperative iCSF leakage and patients without, and the mean cost difference. For the scenario analyses the outcomes are the potential cost savings per 1000 patients when a decrease in iCSF leakage would be achieved. RESULTS: Mean cost difference between patients with and without iCSF leakage was €9665 (95%CI, €5125 to €14 205). The main cost driver was hospital stay with a difference of 8.5 days. A 25% incidence reduction would result in a mean cost saving of -€94 039 (95% CI, -€218 258 to -€7077) per 1000 patients. A maximum cost reduction of -€653 025 (95% CI, -€1 204 243 to -€169 120) per 1000 patients could be achieved if iCSF leakage would be reduced with 75% in all patients, with 72 cases of iCSF leakage avoided. CONCLUSIONS: Postoperative iCSF leakage after intradural cranial surgery increases healthcare costs significantly and substantially. From a health economic perspective preventative measures to avoid iCSF leakage should be pursued.

Fully Automatic Adaptive Meshing Based Segmentation of the Ventricular System for Augmented Reality Visualization and Navigation.

OBJECTIVE: Effective image segmentation of cerebral structures is fundamental to 3-dimensional techniques such as augmented reality. To be clinically viable, segmentation algorithms should be fully automatic and easily integrated in existing digital infrastructure. We created a fully automatic adaptive-meshing-based segmentation system for T1-weighted magnetic resonance images (MRI) to automatically segment the complete ventricular system, running in a cloud-based environment that can be accessed on an augmented reality device. This study aims to assess the accuracy and segmentation time of the system by comparing it to a manually segmented ground truth dataset. METHODS: A ground truth (GT) dataset of 46 contrast-enhanced and non-contrast-enhanced T1-weighted MRI scans was manually segmented. These scans also were uploaded to our system to create a machine-segmented (MS) dataset. The GT data were compared with the MS data using the Sørensen-Dice similarity coefficient and 95% Hausdorff distance to determine segmentation accuracy. Furthermore, segmentation times for all GT and MS segmentations were measured. RESULTS: Automatic segmentation was successful for 45 (98%) of 46 cases. Mean Sørensen-Dice similarity coefficient score was 0.83 (standard deviation [SD] = 0.08) and mean 95% Hausdorff distance was 19.06 mm (SD = 11.20). Segmentation time was significantly longer for the GT group (mean = 14405 seconds, SD = 7089) when compared with the MS group (mean = 1275 seconds, SD = 714) with a mean difference of 13,130 seconds (95% confidence interval 10,130-16,130). CONCLUSIONS: The described adaptive meshing-based segmentation algorithm provides accurate and time-efficient automatic segmentation of the ventricular system from T1 MRI scans and direct visualization of the rendered surface models in augmented reality.